Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps
Efficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2010/932467 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552402630213632 |
|---|---|
| author | Mika Liukkonen Mikko Heikkinen Eero Hälikkä Teri Hiltunen Yrjö Hiltunen |
| author_facet | Mika Liukkonen Mikko Heikkinen Eero Hälikkä Teri Hiltunen Yrjö Hiltunen |
| author_sort | Mika Liukkonen |
| collection | DOAJ |
| description | Efficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components and to create multivariate nonlinear models describing their formation in the process. First, a generic process model was created by using a self-organizing map, which was clustered with the k-means algorithm to create subsets representing the different states of the process. Characteristically, these process states may include high- and low- load situations and transition states where the load is increased or decreased. Then emission models were constructed for both the entire process and for the process state of high boiler load. The main conclusion is that the methodology used is able to reveal such phenomena that occur within the process states and that could otherwise be difficult to observe. |
| format | Article |
| id | doaj-art-f50e78c67e564b33a4ede0691854b53c |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-f50e78c67e564b33a4ede0691854b53c2025-02-03T05:58:55ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322010-01-01201010.1155/2010/932467932467Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing MapsMika Liukkonen0Mikko Heikkinen1Eero Hälikkä2Teri Hiltunen3Yrjö Hiltunen4Department of Environmental Science, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, FinlandDepartment of Environmental Science, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, FinlandFoster Wheeler Energia Oy, Operations, Engineering & Services, P.O. Box 201, 78201 Varkaus, FinlandFoster Wheeler Energia Oy, Operations, Engineering & Services, P.O. Box 201, 78201 Varkaus, FinlandDepartment of Environmental Science, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, FinlandEfficient combustion of fuels with lower emissions levels has become a demanding task in modern power plants, and new tools are needed to diagnose their energy production. The goals of the study were to find dependencies between process variables and the concentrations of gaseous emission components and to create multivariate nonlinear models describing their formation in the process. First, a generic process model was created by using a self-organizing map, which was clustered with the k-means algorithm to create subsets representing the different states of the process. Characteristically, these process states may include high- and low- load situations and transition states where the load is increased or decreased. Then emission models were constructed for both the entire process and for the process state of high boiler load. The main conclusion is that the methodology used is able to reveal such phenomena that occur within the process states and that could otherwise be difficult to observe.http://dx.doi.org/10.1155/2010/932467 |
| spellingShingle | Mika Liukkonen Mikko Heikkinen Eero Hälikkä Teri Hiltunen Yrjö Hiltunen Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps Applied Computational Intelligence and Soft Computing |
| title | Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps |
| title_full | Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps |
| title_fullStr | Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps |
| title_full_unstemmed | Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps |
| title_short | Analysis of Flue Gas Emission Data from Fluidized Bed Combustion Using Self-Organizing Maps |
| title_sort | analysis of flue gas emission data from fluidized bed combustion using self organizing maps |
| url | http://dx.doi.org/10.1155/2010/932467 |
| work_keys_str_mv | AT mikaliukkonen analysisoffluegasemissiondatafromfluidizedbedcombustionusingselforganizingmaps AT mikkoheikkinen analysisoffluegasemissiondatafromfluidizedbedcombustionusingselforganizingmaps AT eerohalikka analysisoffluegasemissiondatafromfluidizedbedcombustionusingselforganizingmaps AT terihiltunen analysisoffluegasemissiondatafromfluidizedbedcombustionusingselforganizingmaps AT yrjohiltunen analysisoffluegasemissiondatafromfluidizedbedcombustionusingselforganizingmaps |